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As video games evolve into expansive, detailed worlds, visual quality becomesessential, yet increasingly challenging. Traditional testing methods, limitedby resources, face difficulties in addressing the plethora of potential bugs.Machine learning offers scalable solutions; however, heavy reliance on largelabeled datasets remains a constraint. Addressing this challenge, we propose anovel method, utilizing unlabeled gameplay and domain-specific augmentations togenerate datasets & self-supervised objectives used during pre-training ormulti-task settings for downstream visual bug detection. Our methodology usesweak-supervision to scale datasets for the crafted objectives and facilitatesboth autonomous and interactive weak-supervision, incorporating unsupervisedclustering and/or an interactive approach based on text and geometric prompts.We demonstrate on first-person player clipping/collision bugs (FPPC) within theexpansive Giantmap game world, that our approach is very effective, improvingover a strong supervised baseline in a practical, very low-prevalence, low dataregime (0.336 $\rightarrow$ 0.550 F1 score). With just 5 labeled "good"exemplars (i.e., 0 bugs), our self-supervised objective alone captures enoughsignal to outperform the low-labeled supervised settings. Building onlarge-pretrained vision models, our approach is adaptable across various visualbugs. Our results suggest applicability in curating datasets for broader imageand video tasks within video games beyond visual bugs.

Weak Supervision for Label Efficient Visual Bug Detection